What is AI?

John McCarthy, who coined the term in 1955, defines Artificial Intelligence (AI) as “the science and engineering of making intelligent machines”.

AI is a branch of Science which is concerned with designing machines that are capable of finding solutions to complex problems in a fashion that resembles human problem-solving abilities. Therefore, AI involves the algorithmic implementation of human intelligence characteristics through a programming language. AI is generally understood as an area of computer science which emphasizes the creation of intelligent machines whose behavior closely resembles the behavior of human beings.

Present day computers and software are not intelligent. They are simply tools with certain types of functionality that inherit the intelligence of the human programmer. The machines will simply execute a code of instructions without ever knowing, understanding or sensing what the instructions really mean and what purpose they serve. AI will have the unique ability to understand, sense and ultimately learn about the nature of the code that it processes. It will be able to comprehend its purpose and will even be able to express an opinion (AI is entitled to its own opinion) about them.

As the field has so many subdivisions, to come up with a unified and general definition of AI is challenging. The overall vision and meaning of AI, however, is to simulate human intelligence processes in computer systems. These processes include learning (the acquisition of information and the rules for using the information), reasoning (using the rules to reach approximate or definite conclusions), and self-correction.

Even though AI is thought of as an offshoot of Computer science, it has many important links to other fields such as Mathematics, Neuroscience, Cognitive science, Biology and Philosophy, among many others. Our ability to combine knowledge from all these fields will ultimately benefit our progress in the quest of creating an intelligent artificial being.

Research associated with artificial intelligence is highly technical and specialized. The core problems of artificial intelligence include programming computers to exhibit certain traits such as

Knowledge

Reasoning

Problem solving

Perception

Learning

Planning

The ability to manipulate and move objects

Knowledge engineering is a core part of AI research. Machines can often act and react like humans only if they have abundant information about the external world. Artificial intelligence must have access to objects, categories and properties, as well as information on how these groups of data interrelate, in order for the implementation of knowledge engineering to be feasible. It is safe to say that enabling common sense, reasoning and problem-solving abilities in machines will be a daunting task.

Machine learning is another core part of AI. Learning without any kind of supervision requires the ability to identify patterns in streams of inputs, whereas learning with adequate supervision involves classification and numerical regressions. Classification determines the category an object belongs to and regression deals with obtaining a set of numerical input or output examples; thereby discovering functions that enable the generation of suitable outputs from respective inputs. Mathematical analysis of machine learning algorithms and their performance is a well-defined branch of theoretical computer science often referred to as computational learning theory.

Machine perception deals with the ability to use sensory input for deducing different aspects of the world, while computer vision refers to the ability of reducing visual input to sub-components such as facial, object and speech recognition.

Lectures from School of Computer science and Information

Brains, Minds and Machines Symposium, May, 2011, at MIT.

The Strong AI Vs Weak AI

The principle behind Strong AI is that the machines could be made to think or in other words could represent human minds in the future. Thus Strong AI claims that in near future we will be surrounded by such kinds of machine which can completely works like human being and machine could have human level intelligence. If that is the case, those machines will have the ability to reason, think and do all functions that a human is capable of doing. On the other hand, the notion behind the principles of the weak AI is that machines can be made to act as if they are intelligent. Weak AI simply states that thinking like features can be easily added to computer to make them more useful tools and this already started to happen. For example, when a human player plays chess against a computer, the human player may feel as if the computer is actually making impressive moves. But the chess application is not thinking and planning at all. All the moves it makes are previously fed in to the computer by a human and that is how it is ensured that the software will make the right moves at the right times.

What is the Difference Between Narrow AI and AGI?

One of the well organized and well thought book on AGI, ‘ADVANCES IN ARTIFICIAL GENERAL INTELLIGENCE: CONCEPTS, ARCHITECTURES AND ALGORITHMS’, defines AGI as follows:

“Artificial General Intelligence, AGI for short, is a term used to stress the “general” nature of the desired capabilities of the systems being researched— as compared to the bulk of mainstream Artificial Intelligence (AI) work, which focuses on systems with very specialized “intelligent” capabilities. While most existing AI projects aim at a certain aspect or application of intelligence, an AGI project aims at “intelligence” as a whole, which has many aspects, and can be used in various situations”.

On the other hand, narrow AI– is the field were the focus is on creating programs that demonstrate intelligence in one or another specialized area, such as chess-playing, medical diagnosis, automobile driving, algebraic calculation, or mathematical theorem proving. Some of these narrow AI programs are extremely successful at what they do.

Hence, the construction of a software program that can solve a variety of complex problems in a variety of diﬀerent domains, and that controls itself autonomously, with its own thoughts, worries, feelings, strengths, weaknesses, and predispositions.

Artiﬁcial General Intelligence (AGI) was the original focus of the AI ﬁeld, but due to the demonstrated diﬃculty of the problem, not many AI researchers are directly concerned with it anymore. Work on AGI has gotten a bit of a bad reputation, as if creating digital general intelligence were analogous to building a perpetual motion machine.

Yet, while the latter is strongly implied to be impossible by well-established physical laws, AGI appears by all known science to be quite possible. Like nanotechnology, it is “merely an engineering problem”, though certainly a very diﬃcult one.

The presupposition of much of the contemporary work on “narrow AI” is that solving narrowly deﬁned sub problems, in isolation, contributes signiﬁcantly toward solving the overall problem of creating real AI. While this is of course true to a certain extent, both cognitive theory and practical experience suggest that it is not as true as is commonly believed. In many cases, the best approach to implementing an aspect of mind in isolation is very diﬀerent from the best way to implement this same aspect of mind in the framework of an integrated AGI-oriented software system. You can read more here -> Probablisitic Logic Network, AGI and OpenCog

Here are some books that are certainly a good start to understand what AGI is.

Branches of AI

What are the branches of AI? Although the science of AI is still an on-going field lacking a clear cut to its scope and boundaries here is a list which describes the commonly accepted branches of Artificial Intelligence Science. Some branches are surely missing, because no one has identified them yet. Some of these may be regarded as concepts or topics rather than full branches.

Logical AI: What a program knows about the world in general the facts of the specific situation in which it must act, and its goals are all represented by sentences of some mathematical logical language. The program decides what to do by inferring that certain actions are appropriate for achieving its goals. Here are some insightful Documents on this specific branch:

Search: AI programs often examine large numbers of possibilities, e.g. moves in a chess game or inferences by a theorem-proving program. Discoveries are continually made about how to do this more efficiently in various domains.

Pattern recognition: When a program makes observations of some kind, it is often programmed to compare what it sees with a pattern. For example, a vision program may try to match a pattern of eyes and a nose in a scene in order to find a face. More complex patterns, e.g. in a natural language text, in a chess position, or in the history of some event are also studied. These more complex patterns require quite different methods than do the simple patterns that have been studied the most.

Representation: Facts about the world have to be represented in some way. Usually languages of mathematical logic are used.

Inference: From some facts, others can be inferred. Mathematical logical deduction is adequate for some purposes, but new methods of non-monotonic inference have been added to logic since the 1970s. The simplest kind of non-monotonic reasoning is default reasoning in which a conclusion is to be inferred by default, but the conclusion can be withdrawn if there is evidence to the contrary. For example, when we hear of a bird, we can infer that it can fly, but this conclusion can be reversed when we hear that it is a penguin. It is the possibility that a conclusion may have to be withdrawn that constitutes the non-monotonic character of the reasoning. Ordinary logical reasoning is monotonic in that the set of conclusions that can the drawn from a set of premises is a monotonic increasing function of the premises. Circumscription is another form of non-monotonic reasoning.

Common sense knowledge and reasoning: In spite of the fact that it has been an active research area since the 1950s, this is a special branch in which AI is farthest from human-level. While there has been considerable progress, e.g. in developing systems of non-monotonic reasoning and theories of action, yet more new ideas are needed.

Learning from experience: Programs do that. The approaches to AI based on connection-ism and neural nets specialize in that. There is also learning of laws expressed in logic. Programs can only learn what facts or behaviors their formalisms can represent, and unfortunately learning systems are almost all based on very limited abilities to represent information. Here is a comprehensive text on Machine Learning, Machine Learning (McGraw-Hill Series in Computer Science).

Here are some books that are certainly a good start to understand what Machine Learning is

Planning: Planning programs start with general facts about the world (especially facts about the effects of actions), facts about the particular situation and a statement of a goal. From these, they generate a strategy for achieving the goal. In the most common cases, the strategy is just a sequence of actions.

Epistemology: This is a study of the kinds of knowledge that are required for solving problems in the world.

Ontology: Ontology is the study of the kinds of things that exist. In AI, the programs and sentences deal with various kinds of objects, and we study what these kinds are and what their basic properties are. Emphasis on ontology begins in the 1990s.

Heuristics: A heuristic is a way of trying to discover something or an idea imbedded in a program. The term is used variously in AI. Heuristic functions are used in some approaches to search to measure how far a node in a search tree seems to be from a goal. Heuristic predicates that compare two nodes in a search tree to see if one is better than the other is. ( i.e. constitutes an advance toward the goal)

Genetic programming: Genetic programming is a technique for getting programs to solve a task by mating random Lisp programs and selecting fittest in millions of generations. Here are some insightful documents on the subject:

Natural Language Processing: This interdisciplinary of AI is also called ‘Computer Speech and Language Processing‘, ‘Human Language Technology‘ or ‘Computational Linguistics‘. The simplified and over all aim of this field is to get computers to perform useful tasks involving human languages. It address problems regarding human to machine or machine to human communications and as well human to human communication. The field’s scope is most recognizable in the recent attempts of enabling machines to channel messages from speech to text form or text to speech form. Some of the very useful contributions from this field include the perfection of conversational agents, dialog systems, machine translation, natural language generation and web based question answering.

Here are some books that are certainly a good start to understand what Natural Language Processing (NLP) is

Applications of AI

What are the applications of AI? Actually Application of AI is numerous and ever increasing. AI has been used in various fields, medical, financial, industrial, security and surveillance, and the agricultural sectors had been using Artificial Intelligence featured software’s and systems for the past two decades. One can say that in today’s manufacturing and service industry, AI applications are deeply embedded even though the field, until very recently, is rarely credited for these successes.

Here are some specific areas where the application of AI is dominantly recognized

Game playing: You can buy machines that can play master level chess for a few hundred dollars. There is some AI in them, but they play well against people mainly through brute force computation–looking at hundreds of thousands of positions. To beat a world champion by brute force and known reliable heuristics requires being able to look at 200 million positions per second.

Speech recognition: In the 1990s, computer speech recognition reached a practical level for limited purposes. Thus, United Airlines has replaced its keyboard tree for flight information by a system using speech recognition of flight numbers and city names. It is quite convenient. On the the other hand, while it is possible to instruct some computers using speech, most users have gone back to the keyboard and the mouse as still more convenient.

Understanding natural language and Semantics : Just getting a sequence of words into a computer is not enough. Parsing sentences is not enough either. The computer has to be provided with an understanding of the domain the text is about, and this is presently possible only for very limited domains.

Computer vision: The world is composed of three-dimensional objects, but the inputs to the human eye and computers’ TV cameras are two-dimensional. Some useful programs can work solely in two dimensions, but full computer vision requires partial three-dimensional information that is not just a set of two-dimensional views. At present there are only limited ways of representing three-dimensional information directly, and they are not as good as what humans evidently use.

Expert systems: A “knowledge engineer” interviews experts in a certain domain and tries to embody their knowledge in a computer program for carrying out some task. How well this works depends on whether the intellectual mechanisms required for the task are within the present state of AI. When this turned out not to be so, there were many disappointing results. One of the first expert systems was MYCIN in 1974, which diagnosed bacterial infections of the blood and suggested treatments. It did better than medical students did or practicing doctors, provided its limitations were observed. Namely, its ontology included bacteria, symptoms, and treatments and did not include patients, doctors, hospitals, death, recovery, and events occurring in time. Its interactions depended on a single patient being considered. Since the experts consulted by the knowledge engineers knew about patients, doctors, death, recovery, etc., it is clear that the knowledge engineers forced what the experts told them into a predetermined framework. In the present state of AI, this has to be true. The usefulness of current expert systems depends on their users having common sense.

Heuristic classification: One of the most feasible kinds of expert system given the present knowledge of AI is to put some information in one of a fixed set of categories using several sources of information. An example is advising whether to accept a proposed credit card purchase. Information is available about the owner of the credit card, his record of payment and also about the item he is buying and about the establishment from which he is buying it (e.g., about whether there have been previous credit card frauds at this establishment).

Weather Forecasting: Neural networks are used for predicting weather conditions. Previous data is fed to a neural network that learns the pattern and uses that knowledge to predict weather patterns.

AI in Heavy Industries and Space: Robotics and cybernetics have taken a leap, combined with expert systems. An entire process is now totally automated, controlled, and maintained by a computer system in car manufacturing, machine tool production, computer chip production, and almost every high-tech process. They carry out dangerous tasks like handling hazardous radioactive materials. Robotic pilots carry out complex maneuvering techniques of unmanned spacecraft sent in space. Japan is the leading country in the world in terms of robotics research and use. All the rovers that landed on Mars had an in-built operating system that could control, plan, and strategize their movements, as well as deploy on-board equipment, without help or intervention from Earth.

Algorithmic Trading: Software programs that can predict trends in the stock market have been created, and are proved to beat humans in terms of predictive power. Algorithmic trading is widely used by investment banks, institutional investors, and large firms to carry out rapid buying and selling activity, to capitalize on lucrative opportunities that arise in the global markets. Not only are the software programs predicting trends, but they are also making decisions, based on per-programmed rules. The machine learning software can detect patterns that humans may not see. Heaps of data from decades of world stock market history are fed to these algorithms to find patterns that can offer an insight into making future predictions. Banks use intelligent software applications to screen and analyze financial data.

Swarm Intelligence: This is an approach to, as well as application of AI, similar to a neural network. Here, programmers study how intelligence emerges in natural systems like swarms of bees even though on an individual level, a bee just follows simple rules. They study relationships in nature like the prey-predator relationships that give an insight into how intelligence emerges in a swarm or collection from simple rules at an individual level. They develop intelligent systems by creating agent programs that mimic the behavior of these natural systems.

For further understanding of the areas, the ways, the techniques, the market, and the customers of Artificial Intelligence and its application, here are some insightful documents on the subject:

Most of the applications discussed above are focused on automating tasks through machine learning and the brute force of computing power. Machines are still not close to actually understanding the meaning behind the data or making analogous connections between different types of information, which is the first step towards real intelligence.

The Exponential Advancement of AI

“It may sound strange to say that AI is advancing exponentially. After all, where are Hal 9000, R2D2 and C-3PO? While progress towards human level AGI hasn’t been rapid, a broader look at AI shows remarkable growth. The quantity of AI-related applications, since the invention of AI 50 years ago, is dramatically larger.

AI players are a common feature in video games. The world’s best chess player is an AI. Search engines like Google and Bing help us sift through billions and billions of articles available online; and they base their work on the research of hundreds of AI PhDs. They don’t brand themselves as AI, but they are. We have guided missiles run by AI that can locate desirable targets and automatically destroy them. The military also uses AI planning and scheduling software to handle logistics, supply chain management, and a host of prosaic tasks.

On Wall Street, computer programs now handle the majority of stock trading; computers trade a huge percentage of the world’s money in global financial markets. Many are simple programs using statistics to find patterns in the market –obeying rules prescribed to them and acting in a simplistic, automated way. Nevertheless, others are more advanced, sophisticated AI-based trading systems.

From my perspective as an AI researcher, the pace of progress in AI can sometimes seem frustratingly slow. I think we’re not progressing faster toward superhuman AGI because of largely psychological and financial reasons, rather than scientific ones. On the other hand, if I look at the bigger picture, at the breadth of human history, the advancement of AI over the last half century has been pretty impressive. Moreover, the advancement of supporting technologies capable of enabling ongoing AI and AGI progress (computer hardware and software, specialized narrow AI, biotech, nanotechnology, and quantum computing) has been tremendous”.

Excerpt from the soon to be released book, “Faster Than You Think” by Dr. Ben Goertzel

The Prospects of AI from a Basic Approach to the Field

International interactions are primarily focused on monitoring and controlling global economy.

Global Economy ― as we know it ― and contemporary economic thought, along with production science, are on the verge of radical transformation.

Why?

Dominant economic systems are currently characterized by specific production methods, energy resources, natural resource allocation and other key elements in the global economic structure, that are being subject to profound challenges. The global economic crisis which started at the end of the first decade of the 21st century has demonstrated the inadequacy of protection mechanisms in the present economic establishment. The world is crying out for a change. A paradigm shift to a more resilient economic model is imminent.

Currently, computer science is functioning as an essential energy source for every technological discovery. Technological innovation, in return, controls global economy in the relevant fields where innovation occurs. Furthermore, computer science is designing powerful tools that will have great impact on human affairs. The field of computer science is laboriously pursuing the creation of Artificial Intelligence.

Undoubtedly, Science and technology play a pivotal role in tackling global and regional problems such as climate change, environmental deterioration, infectious diseases, food security, and the disparity between rich and poor.

It has been demonstrated that advancements in Science and Technology can aid the achievement of such goals as the eradication of poverty and capability enhancement for independent development. Simply put, progress in Science and Technology is the driving force for sustainable economic growth; technological and scientific breakthroughs are the key to leapfrogging development.

One may go as far as suggesting that the present status of science is nothing more but the reflection of achievements in the field of computer science. As it seems, science and technology are also providing feedback which drives the development of computer science. Seeing as this interrelationship between computer science and other scientific fields exists, it follows that we will eventually stumble upon the creation of AI.

Meanwhile, countries and institutions worldwide as well as many established scientists, futurists and even competing corporations are combining efforts to create AI.

Furthermore, cooperation has become an important approach for each country in order to build partnerships with mutual benefits. This new paradigm will soon become an integrated approach in the field of computer science that will take precedence over all forms of inter-organizational and international cooperation, and will aspire to transform the global economic structure.

As an example, one can mention the colossal Science and Technology Partnership Program (CASTEP) between China and Africa, launched in late 2009 as one of the eight measures that the Chinese Government has taken to strengthen Sino-African cooperation.

Based on such facts and while observing an exponential growth in computer science funding, it is permissible to say that the field is heading toward a crucial discovery which could very well be the most significant one in the entire history of humankind. In the coming few decades, mankind will most certainly achieve the creation of Artificial Intelligence: A computer program which can think, act, decide and learn in the same way that the human brain does.

Artificial Intelligence will then guide, formulate, calculate, supervise, and eventually take over the control of the global production system.

AI will be intensively utilized in the sectors of military, health, industry, space, agriculture and even the service sector. In the long-run, socioeconomic development and the task of facilitating sustainable global economic growth will be dominated by AI.

Although it is impossible to predict the end result of discovering AI, it is pretty obvious that this new breakthrough in Science and Technology will enhance humanity to the extent that we will no longer recognize any resemblance of our AI enhanced world to the world as it existed before AI was discovered.

Yet there is always the spine-chilling shadow of catastrophic implications being cast on such endeavors . Such catastrophic scenarios could manifest in the form of a computer virus with the ability to infect AI source code, delivering a payload that generates malevolent intentions towards humanity or a a scenario where a group of people employ AI as a nightmarish apocalyptic weapon of mass destruction.

Yet again, the possibility of negative output has always lingered behind every major technological discovery; eschatological fears and negativity are unlikely to hinder the world from discovering AI since there is no denying of its social benefit and the validity of the science in this field which is as concrete as the science in every other computer science subset.

Major Areas Affected with AI: Sectors which also are vitally important in today’s Africa

Despite the obvious changes which are sweeping across the world’s economic and sociopolitical status, early stage AI is also expected to have great impact on Africa.

Computer and Software business will be transformed dramatically.

The Manufacturing Sector will be transformed radically; robotized and fully automated industry will be established. Humans may become obsolete in the production process of industrial goods, which can go either way in terms of socioeconomic impact.

Telecommunications, while important to humans, will be crucial for digital AI systems who will be able to process data from all over the planet, and beyond, by utilizing telecommunication networks.

The Military will be very involved with AI and it is expected to be one of the major stakeholders in the science.

Agriculture, as we know it, might cease to exist. The creation of new and better forms of food through the genetic manipulation of various plants and animals will transform agriculture in the coming AI age.

The Pharmaceuticals & Health Care Sector will be revolutionized. AI will be implemented in drug discovery and other genome advancements. This sector will be one of the hot spots in the AI era.

The Finance world (Banking, Stock market, and Insurance) and its pivotal role in economy will also be altered by the overreaching AI applications. Finance these days is largely based on advanced mathematics, but the mathematical formulas used are mere approximations to the chaotic nature of reality. AI will employ direct mental connections to basic algorithmic software tools that will apply financial mathematics in more sophisticated ways and by performing superior data analysis through reducing unrealistic assumptions. Such changes will have a huge transformative impact on the finance sector.